Abstract: There has been a gap between artificial intelligence and human intelligence. In this paper, we identify three key elements forming human intelligence, and suggest that combines these elements and is thus a way to bridge the gap. Prior researches in artificial intelligence either specify by human experts, or take as a qualitative explanation for the model. This paper aims to learn directly. We tackle three main challenges: representation, objective function, and learning algorithm. Specifically, we propose a partition structure that contains pre-allocated neurons; we formulate learning as a constrained optimization problem, which integrates properties; we develop a network evolution algorithm to solve this problem. This complete framework is named ONE (Optimization via Network Evolution). In our experiments on MNIST, ONE shows elementary human- like intelligence, including low consumption, knowledge sharing, and lifelong learning.



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